Neural code representation. To learn a neural code representation, we learn
a neural encoding function \(\textbf {e}(t_i)\)  that maps each token \(t_i\)  to a vector representation.
For this, we employ a BPE subtoken encoder [1]} (with a vocabulary of 10K subtokens) to obtain an initial token embedding
by averaging the embedding of its subtokens.
Afterwards, we encode the sequence of token embeddings via a Transformer encoder [2]} (with relative position encodings [3]}) to obtain a contextualized vector representation \(\mathbf {r}_i = \textbf {e}(t_i)\) .
